|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: main_intent_test |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# main_intent_test |
|
|
|
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
|
|
|
## Model description |
|
|
|
Custom data generated labeling text according to these five categories. |
|
Five categories represent the five essential intents of a user for the ACTS scenario. |
|
|
|
- Connect : Greetings and introduction with the student |
|
- Pump : Asking the student for information |
|
- Inform : Providing information to the student |
|
- Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback) |
|
- None : Not related to scenario |
|
|
|
Takes a user input of string text and classifies it according to one of five categories. |
|
|
|
## Intended uses & limitations |
|
|
|
|
|
from transformers import pipeline |
|
classifier = pipeline("text-classification",model="mp6kv/main_intent_test") |
|
|
|
|
|
output = classifier("great job, you're getting it!") |
|
|
|
score = output[0]['score'] |
|
|
|
label = output[0]['label'] |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 2e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.17.0 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 1.18.3 |
|
- Tokenizers 0.11.6 |
|
|
